Genomic prediction modeling of soybean biomass using UAV‐based remote sensing and longitudinal model parameters

Toda, Yusuke; Kaga, Akito; Kajiya‐Kanegae, Hiromi; Hattori, Tomohiro; Yamaoka, Shuhei; Okamoto, Masanori; Tsujimoto, Hisashi; Iwata, Hiroyoshi

doi:10.1002/tpg2.20157

Cited by 18 publications

(19 citation statements)

References 31 publications

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“…Nevertheless, this study has shown that GF is a useful trait to describe the growth of soybean germplasm. In genomic prediction, the inclusion of canopy area, which is proportional to GF, has been reported to improve the prediction of biomass in soybean ( Toda et al, 2021a ). Thus, GF can be considered a useful index of genetic variation in plant growth.…”

Section: Discussionmentioning

confidence: 99%

“…Finally, the GP model is applied to predict the parameters of the GF dynamics model under a range of scenarios to illustrate the potential of the proposed method. A similar experiment was conducted in an earlier paper in which UAV-RS data was used as secondary traits to predict biomass ( Toda et al, 2021a ), while this study developed prediction models of growth curve itself.…”

Section: Introductionmentioning

confidence: 99%

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Genomic Prediction of Green Fraction Dynamics in Soybean Using Unmanned Aerial Vehicles Observations

et al. 2022

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With the widespread use of high-throughput phenotyping systems, growth process data are expected to become more easily available. By applying genomic prediction to growth data, it will be possible to predict the growth of untested genotypes. Predicting the growth process will be useful for crop breeding, as variability in the growth process has a significant impact on the management of plant cultivation. However, the integration of growth modeling and genomic prediction has yet to be studied in depth. In this study, we implemented new prediction models to propose a novel growth prediction scheme. Phenotype data of 198 soybean germplasm genotypes were acquired for 3 years in experimental fields in Tottori, Japan. The longitudinal changes in the green fractions were measured using UAV remote sensing. Then, a dynamic model was fitted to the green fraction to extract the dynamic characteristics of the green fraction as five parameters. Using the estimated growth parameters, we developed models for genomic prediction of the growth process and tested whether the inclusion of the dynamic model contributed to better prediction of growth. Our proposed models consist of two steps: first, predicting the parameters of the dynamics model with genomic prediction, and then substituting the predicted values for the parameters of the dynamics model. By evaluating the heritability of the growth parameters, the dynamic model was able to effectively extract genetic diversity in the growth characteristics of the green fraction. In addition, the proposed prediction model showed higher prediction accuracy than conventional genomic prediction models, especially when the future growth of the test population is a prediction target given the observed values in the first half of growth as training data. This indicates that our model was able to successfully combine information from the early growth period with phenotypic data from the training population for prediction. This prediction method could be applied to selection at an early growth stage in crop breeding, and could reduce the cost and time of field trials.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Genomic Prediction of Green Fraction Dynamics in Soybean Using Unmanned Aerial Vehicles Observations

et al. 2022

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“…Nevertheless, this study has shown that GF is a useful trait to describe the growth of soybean germplasm. In genomic prediction, the inclusion of canopy area, which is proportional to GF, has been reported to improve the prediction of biomass in soybean (Toda et al, 2021). Thus, GF can be considered a useful index of genetic variation in plant growth.…”

Section: Uav-rs As a Tool To Evaluate Growth Patternsmentioning

confidence: 99%

Genomic prediction of green fraction dynamics in soybean using UAV observations.

et al. 2021

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With the widespread use of high-throughput phenotyping systems, growth process data are expected to become more easily available. By applying genomic prediction to growth data, it will be possible to predict the growth of untested genotypes. Predicting the growth process will be useful for crop breeding, as variability in the growth process has a significant impact on the management of plant cultivation. However, the integration of growth modeling and genomic prediction has yet to be studied in depth. In this study, we implemented new prediction models to propose a novel growth prediction scheme. Phenotype data of 198 soybean germplasm genotypes were acquired for three years in experimental fields in Tottori, Japan. The longitudinal changes in the green fractions were measured using UAV remote sensing. Then, a dynamic model was fitted to the green fraction to extract the dynamic characteristics of the green fraction as five parameters. Using the estimated growth parameters, we developed models for genomic prediction of the growth process and tested whether the inclusion of the dynamic model contributed to better prediction of growth. Our proposed models consist of two steps: first, predicting the parameters of the dynamics model with genomic prediction, and then substituting the predicted values for the parameters of the dynamics model. By evaluating the heritability of the growth parameters, the dynamic model was able to effectively extract genetic diversity in the growth characteristics of the green fraction. In addition, the proposed prediction model showed higher prediction accuracy than conventional genomic prediction models, especially when the future growth of the test population is a prediction target given the observed values in the first half of growth as training data. This indicates that our model was able to successfully combine information from the early growth period with phenotypic data from the training population for prediction. This prediction method could be applied to selection at an early growth stage in crop breeding, and could reduce the cost and time of field trials.

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“…Many time-series models have been proposed for analyzing crop phenology. Such models include shape-model fitting (Sakamoto et al, 2013;Zhou et al, 2020), random regression with the Legendre polynomial (Campbell et al, 2018;Campbell et al, 2019), segmented linear regression (Toda et al, 2021), and non-linear growth curves (Chang et al, 2017;Grados et al, 2020;Poudel et al, 2022). Anderson et al (2019) applied a three-parameter logistic model (S-shape non-linear curve) to maize CH time-series data measured by UAV over 1 year, applied a linear mixed effects (LME) model to the logistic parameters, decomposed the parameter variance into genetic and environmental effects: they showed that some of the parameters could be used as predictors of grain yield.…”

Section: Introductionmentioning

confidence: 99%

“…Many time-series models have been proposed for analyzing crop phenology. Such models include shape-model fitting ( Sakamoto et al., 2013 ; Zhou et al., 2020 ), random regression with the Legendre polynomial ( Campbell et al., 2018 ; Campbell et al., 2019 ), segmented linear regression ( Toda et al., 2021 ), and non-linear growth curves ( Chang et al., 2017 ; Grados et al., 2020 ; Poudel et al., 2022 ). Anderson et al.…”

Section: Introductionmentioning

confidence: 99%

Prediction of heading date, culm length, and biomass from canopy-height-related parameters derived from time-series UAV observations of rice

et al. 2022

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Unmanned aerial vehicles (UAVs) are powerful tools for monitoring crops for high-throughput phenotyping. Time-series aerial photography of fields can record the whole process of crop growth. Canopy height (CH), which is vertical plant growth, has been used as an indicator for the evaluation of lodging tolerance and the prediction of biomass and yield. However, there have been few attempts to use UAV-derived time-series CH data for field testing of crop lines. Here we provide a novel framework for trait prediction using CH data in rice. We generated UAV-based digital surface models of crops to extract CH data of 30 Japanese rice cultivars in 2019, 2020, and 2021. CH-related parameters were calculated in a non-linear time-series model as an S-shaped plant growth curve. The maximum saturation CH value was the most important predictor for culm length. The time point at the maximum CH contributed to the prediction of days to heading, and was able to predict stem and leaf weight and aboveground weight, possibly reflecting the association of biomass with duration of vegetative growth. These results indicate that the CH-related parameters acquired by UAV can be useful as predictors of traits typically measured by hand.

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Genomic prediction modeling of soybean biomass using UAV‐based remote sensing and longitudinal model parameters

Cited by 18 publications

References 31 publications

Genomic Prediction of Green Fraction Dynamics in Soybean Using Unmanned Aerial Vehicles Observations

Genomic Prediction of Green Fraction Dynamics in Soybean Using Unmanned Aerial Vehicles Observations

Genomic prediction of green fraction dynamics in soybean using UAV observations.

Prediction of heading date, culm length, and biomass from canopy-height-related parameters derived from time-series UAV observations of rice

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